Machine Learning in Metrology Measurements

ABSTRACT

Metrology methods and targets are provided, that expand metrological procedures beyond current technologies into multi-layered targets, quasi-periodic targets and device-like targets, without having to introduce offsets along the critical direction of the device design. Machine learning algorithm application to measurements and/or simulations of metrology measurements of metrology targets are disclosed for deriving metrology data such as overlays from multi-layered target and corresponding configurations of targets are provided to enable such measurements. Quasi-periodic targets which are based on device patterns are shown to improve the similarity between target and device designs. Offsets are introduced only in non-critical direction and/or sensitivity is calibrated to enable, together with the solutions for multi-layer measurements and quasi-periodic target measurements, direct device optical metrology measurements.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication No. 62/546,509 filed on Aug. 16, 2017, which is incorporatedherein by reference in its entirety.

BACKGROUND OF THE INVENTION 1. Technical Field

The present invention relates to the field of metrology, and moreparticularly, to the derivation of metrology measurements and improvingtargets and simulation results.

2. Discussion of Related Art

Metrology targets and methods aim at deriving measurements whichrepresent the production accuracy of designed devices. Metrology facesthe challenges of yielding measurable signals which reflect accuratelyproperties of the devices, at a rate that is high enough and a realestate that is low enough, to minimize the hindrances to the production.Current metrology overlay (OVL) algorithms use special targets that haveperiodic structures in two layers, which are offset differently indifferent target cells.

U.S. Patent Application Publication No. 2016/0266505, which isincorporated herein by reference in its entirety, discloses on-deviceoptical measurements and on-the-fly model-based measurement algorithms.

U.S. Patent Application Publication No. 2009/0244538, which isincorporated herein by reference in its entirety, discloses alithographic apparatus arranged to transfer a pattern from a patterningdevice onto a substrate with a reference set of gratings provided in thesubstrate, the reference set including two reference gratings havingline elements in a first direction and one reference grating having lineelements in a second, perpendicular, direction. US 2009/0244538 requiresidentical (or very similar) designs for x and y in order to calculatethe sensitivity of one direction and apply it to the second direction.However, this x-y design symmetry breaks in the device due to electricalfunctionality needs (and also the lithography process in critical layersis not symmetric).

U.S. Patent Application Publication No. 2011/0255066, which isincorporated herein by reference in its entirety, discloses measuringoverlays using multiple targets in multiple fields, assuming that theoverlay sensitivity of targets across the fields of the wafer isconstant, ignoring intra-field process variations.

Young-Nam Kim et al. 2009 (Device based in-chip critical dimension andoverlay metrology, Optics Express 17:23, 21336-21343), which isincorporated herein by reference in its entirety, discloses amodel-based in-chip optical metrology technique that allows directmeasurement of both critical dimensions and overlay displacement errorsin the DRAM manufacturing process, performed on the actual semiconductordevices without requiring special target structures.

SUMMARY OF THE INVENTION

The following is a simplified summary providing an initial understandingof the invention. The summary does not necessarily identify key elementsnor limits the scope of the invention, but merely serves as anintroduction to the following description.

One aspect of the present invention provides a method of directlymeasuring metrology parameters on devices, the method comprising: (i)measuring at least one metrology parameter from at least a portion of adevice design that is selected to have a plurality of irregularlyrepeating units, having specified features such as different sets oflines and cuts, along at least one direction of the portion, and (ii)applying at least one machine learning algorithm to calibratesensitivity using at least one of: an intensity of diffraction ordersorthogonal to the at least one direction, introduced offsets along anon-critical direction, target cells with introduced offsets adjacent tothe device portion, and at least one sensitivity calibration target,wherein the measuring is carried out scatterometrically on a pluralityof targets to provide layer-specific metrology parameters, at least oneof the targets being part of the device portion having N>2 overlappinglayers, wherein the plurality of targets comprises at least one of: Ncell pairs, each pair having opposite offsets at a different layer; Ncells with selected intended offsets; N or fewer cells with selectedintended offsets configured to utilize pupil information; and N-cellcalibration targets alongside between N−1 and two overlay targets.

One aspect of the present invention provides a method comprising:configuring a multi-layered metrology target to have a plurality, M, oftarget cells over at least three, N≤M, target layers, each cell havingat least one periodic structure in each layer and configuring theperiodic structures of each cell to be offset with respect to each otherby specified offsets, measuring, scatterometrically, at least Mdifferential signals from the multi-layered metrology target, andapplying at least one machine learning algorithm to the differentialsignals and to the specified offsets, to calculate scatterometry overlay(SCOL) metrology parameters from the M measurements of the multi-layeredmetrology target by solving a set of M equations that relate the SCOLmetrology parameters to the differential signals and to the specifiedoffsets.

One aspect of the present invention provides a method comprisingmeasuring at least one metrology parameter in at least one target cellwithout introducing an intended offset along a critical measurementdirection into the at least one target cell by applying at least onemachine learning algorithm to calibrate at least one sensitivityparameter using offsets in at least one of: (i) an orthogonal,non-critical measurement direction and (ii) at least one additionaltarget cell other than the at least one target cell.

One aspect of the present invention provides a method of directlymeasuring metrology parameters on devices which combines theabove-mentioned methods synergistically.

These, additional, and/or other aspects and/or advantages of the presentinvention are set forth in the detailed description which follows;possibly inferable from the detailed description; and/or learnable bypractice of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

For a better understanding of embodiments of the invention and to showhow the same may be carried into effect, reference will now be made,purely by way of example, to the accompanying drawings in which likenumerals designate corresponding elements or sections throughout.

In the accompanying drawings:

FIG. 1A is a high-level schematic overview illustration of multilayertargets and measurement methods thereof, according to some embodimentsof the invention.

FIG. 1B is a high-level schematic illustration of two types ofmultilayer targets and measurement methods thereof, according to someembodiments of the invention.

FIG. 2 is a high-level schematic illustration of multilayer targets,according to some embodiments of the invention.

FIGS. 3A and 3B are high-level schematic illustrations of multilayertargets, according to some embodiments of the invention.

FIG. 4 is a high-level flowchart illustrating method, according to someembodiments of the invention.

FIGS. 5A-5D and 6A-6F are high-level schematic illustrations ofquasiperiodic SCOL targets, according to some embodiments of theinvention.

FIGS. 7A and 7B present simulation results of the effect of the noiseintroduced by the non-periodic target design on the first orderamplitude, according to some embodiments of the invention.

FIG. 8 is a high-level flowchart illustrating method, according to someembodiments of the invention.

FIGS. 9 and 10 are high-level schematic illustrations of devicealignments, according to some embodiments of the invention.

FIG. 11 is a high-level schematic illustration of leading diffractionorders along the non-critical and critical measurement directions,according to some embodiments of the invention.

FIG. 12 is a high-level schematic illustration of a target incorporatingan offset-less device portion, according to some embodiments of theinvention.

FIG. 13 presents a table with exemplary simulation results of theresulting sensitivity values for different combinations of the first andsecond cells designs, according to some embodiments of the invention.

FIG. 14 is a high-level flowchart illustrating a method of measuringoverlays without introducing intended shift along the criticaldirections, according to some embodiments of the invention.

FIG. 15 is a high-level schematic illustration of a composite devicetarget, according to some embodiments of the invention.

FIG. 16 is a high-level flowchart illustrating an integrative method ofmeasuring device overlays directly on the device, according to someembodiments of the invention.

FIG. 17 is a high-level flowchart illustrating a method of applyingmachine learning algorithms to any of the disclosed methods, accordingto some embodiments of the invention.

DETAILED DESCRIPTION OF THE INVENTION

Prior to the detailed description being set forth, it may be helpful toset forth definitions of certain terms that will be used hereinafter.

The terms “metrology target” or “target” as used herein in thisapplication, are defined as any structure designed or produced on awafer which is used for metrological purposes. Specifically, overlaytargets are designed to enable measurements of the overlay between twoor more layers in a film stack that is produced on a wafer. Exemplaryoverlay targets are scatterometry targets, which are measured byscatterometry at the pupil plane and/or at the field plane, and imagingtargets.

Exemplary scatterometry targets may comprise two or more either periodicor aperiodic structures (referred to in a non-limiting manner asgratings) which are located at different layers and may be designed andproduced one above the other (termed “grating-over-grating”) or oneadjacent to another from a perpendicular point of view, termed“side-by-side”). Target designs include one or more cells, the term“cell” as used herein in this application, is defined as a part of atarget that is used to derive a measurement signal. Common scatterometrytargets are referred to as SCOL (scatterometry overlay) targets, DBO(diffraction based overlay) targets and so forth. Common imaging targetsare referred to as Box-in-Box (BiB) targets, AIM (advance imagingmetrology) targets, AIMid targets, Blossom targets and so forth. It isnoted that in the present invention, SCOL is related to as beingmodel-free in the sense that the details of measured stack must not beknown prior to the measurements and that no modelling of the target isnecessarily required in order to extract the parameters. It is furthernoted that the invention is not limited to any of these specific types,but may be carried out with respect to any target design.

Target elements comprise periodic structures, having elements repeatingat one or more pitches, such as gratings. Certain metrology targetsexhibit an “induced offset”, also termed “intended offset”, “designedoffset” or “designed misalignment”, which is, as used herein in thisapplication, an intentional shift or overlay between the periodicstructures of the target. The term “overlay” as used herein in thisapplication, is defined as the overall offset, i.e. the intended offsetplus an unintentional offset, between two layers of a target or adevice. The unintentional offset may thus be derived by subtracting theintended shift from the measured overlay. Overlay targets are typicallydesigned to have pairs of cells, the cells of each pair having equal andopposite intended offsets, denoted ±f₀.

The term “periodic” as used herein in this application with respect to atarget or a target structure, is defined as them having a recurringpattern. The term “strictly periodic” as used herein in this applicationrefers to a target or a target structure which have a recurring unitcell which is identical at all recurrences. The term “quasi-periodic” asused herein in this application refers to a target or a target structurewhich have a recurring pattern which does not have a recurring unit cellbut rather exhibits a basic pattern such as a grid that underlies therespective element as well as multiple aberrations, for example, changesin the length, width or details of the recurring pattern, and/or changesin grid parameters and features. These aberrations may be judiciouslyselected (as explained) and/or may be random and/or may reflect designconsiderations. The effect of the aberrations on the signal, i.e., thedifference between signals derived from quasi-periodic targets andequivalent strictly periodic target is referred to in this applicationas “noise”, which may have random and systematic components. The noisemay be considered as part of the inaccuracy defined below (with respectto the strictly periodic design) and/or may be treated independentlythereof.

The terms “device” or “device design” as used herein in thisapplication, are defined as any part of the wafer which provides anoperating electronic circuit, such as e.g., memory devices or logicdevices. The term “critical direction” as used herein in thisapplication, is defined as a direction in the device design which issensitive to small offsets between layers (e.g., in the order ofmagnitude of 1 nm), with the device possibly malfunctioning if suchoffsets occur. The term “non-critical direction” as used herein in thisapplication, is defined as a direction in the device design which cantolerate small offsets (e.g., in the order of magnitude of 1 nm),without the device malfunctioning if such offsets occur.

The term “measurement direction” as used herein in this application, isdefined as a direction along which the overlay is measured. Whenperiodic targets are used there must be periodicity in the measurementdirection. The pitch is in the order of hundreds up to a few thousandsof nanometers. In addition to this coarse pitch there may be a typicallymuch smaller segmentation pitch of the features and\or a coarse pitch inthe orthogonal direction.

It is noted that while the disclosure is aimed at optical illuminationradiation, it may be extended to applications in which the illuminationradiation is at very short wavelengths such as x ray and/or includeparticles beams.

With respect to metrology measurements, the term “differential signal”as used herein in this application, is defined as the intensitydifference between two signals, such as the +1 and −1 diffraction ordersignals, measured from a target. The term “sensitivity” as used hereinin this application, is defined as a ratio, or relation, between thedifferential signal and the overall offset, or overlay, between periodicstructures along a respective measurement direction. The term“inaccuracy” as used herein in this application, is defined as adifference between a result of a measurement and the true value of themeasured quantity (measurand). It is underlined that while the presentedmodels are mostly linear, for clarity reasons, linearity is non-limitingin the sense that the algorithms may be adapted to utilize non-linearmodel, which are therefore part of the present disclosure as well.

In the following description, various aspects of the present inventionare described. For purposes of explanation, specific configurations anddetails are set forth in order to provide a thorough understanding ofthe present invention. However, it will also be apparent to one skilledin the art that the present invention may be practiced without thespecific details presented herein. Furthermore, well known features mayhave been omitted or simplified in order not to obscure the presentinvention. With specific reference to the drawings, it is stressed thatthe particulars shown are by way of example and for purposes ofillustrative discussion of the present invention only, and are presentedin the cause of providing what is believed to be the most useful andreadily understood description of the principles and conceptual aspectsof the invention. In this regard, no attempt is made to show structuraldetails of the invention in more detail than is necessary for afundamental understanding of the invention, the description taken withthe drawings making apparent to those skilled in the art how the severalforms of the invention may be embodied in practice.

Before at least one embodiment of the invention is explained in detail,it is to be understood that the invention is not limited in itsapplication to the details of construction and the arrangement of thecomponents set forth in the following description or illustrated in thedrawings. The invention is applicable to other embodiments that may bepracticed or carried out in various ways. Also, it is to be understoodthat the phraseology and terminology employed herein is for the purposeof description and should not be regarded as limiting.

Unless specifically stated otherwise, as apparent from the followingdiscussions, it is appreciated that throughout the specificationdiscussions utilizing terms such as “processing”, “computing”,“calculating”, “determining”, “enhancing” or the like, refer to theaction and/or processes of a computer or computing system, or similarelectronic computing device, that manipulates and/or transforms datarepresented as physical, such as electronic, quantities within thecomputing system's registers and/or memories into other data similarlyrepresented as physical quantities within the computing system'smemories, registers or other such information storage, transmission ordisplay devices.

Embodiments of the present invention provide efficient and economicalmethods and targets for measuring metrology parameters, in particularoverlays and particularly using model-free far field optical metrology,using device designs directly. Specifically, the following disclosureovercomes the three major barriers that prohibit prior art direct devicemeasurements, namely the multi-layer character of device designs, thenon-periodic nature of device designs and the inherent constraint ofhaving to avoid introduction of intended offset into actual devicedesigns (in order not to damage their electrical properties andperformance).

Metrology methods and targets are provided, that expand metrologicalprocedures beyond current technologies into multi-layered targets,quasi-periodic targets and device-like targets, without having tointroduce offsets along the critical direction of the device design.Machine learning algorithm application to measurements and/orsimulations of metrology measurements of metrology targets are disclosedfor deriving metrology data such as overlays from multi-layered targetand corresponding configurations of targets are provided to enable suchmeasurements. Quasi-periodic targets which are based on device patternsare shown to improve the similarity between target and device designs.Offsets are introduced only in non-critical direction and/or sensitivityis calibrated to enable, together with the solutions for multi-layermeasurements and quasi-periodic target measurements, direct deviceoptical metrology measurements.

The methods and targets are exemplified for optical scatterometryoverlay (SCOL) measurements of device structures, which is a fastnon-destructive overlay (OVL) technique. Its main limitation is the needfor special targets due to its limited resolution. These metrologytargets may have bad correlation with the actual device structuresbecause of the big deviations between their designs and locations. It istherefore desired to measure directly the device in order to betterreflect its OVL and other possible parameters of interest. The sectionlabeled “multi-layer targets” provides methodologies which enable SCOLmeasurements of overlapping multiple parallel patterning. The sectionlabeled “quasiperiodic targets” discloses how to handle device designsthat lack a unit cell and are not strictly periodic as well as how tohandle the signal to noise ratio in SCOL. Finally, the section labeled“avoiding offsets in device targets” presents innovative methods forsensitivity calculation without damaging the electrical properties. Thedisclosed methods, algorithms and target designs are combinedsynergistically into a complete solution for on-device optical OVLmeasurements.

The inventors have found out that while U.S. Patent ApplicationPublication No. 20160266505 discloses analytic methods and models(mostly physics-based) that solve issues related to devicecharacteristics (e.g., having complex multi-patterning structure, notbeing periodic, having electrical properties which are damaged byintended offsets required for SCOL targets) which are problematic in thecontext of overlay measurements and provides corresponding targetdesigns—a complementary or alternative approach for extractinginformation from measurement signals may be using machine learningapproaches. For example, machine learning algorithms may be trained on acluster of signals and the corresponding desired metrology values tofind correlations and functions to extract the required parameters. Forexample, machine learning algorithms may be used to reduce the number ofrequired cells in multi-layer target designs, possibly down to a singlecell.

In certain embodiments, the machine learning algorithms may be used toderive overlay sensitivity calibration based on on-the-fly information,where the information may come from different sources, such as:additional diffraction orders, orthogonal diffraction orders, a secondtarget with different design etc. The machine learning methodology maybe applied in relation to other methods for extracting the respectiveinformation to provide mutual enhancement of the efficiency of therespective method.

Multi-Layer Targets

First, multi-layer targets are disclosed together with correspondingmeasurement and signal derivation algorithms and machine learningalgorithms based thereon, which enable their measurement with no orsmall throughput/real-estate penalty. The targets are discussed in anon-limiting manner in a one dimensional context and for scatterometryoverlay (SCOL) targets. Such targets and methods are expected to improveupon current technologies at least on the following aspects: (i) Thedesign and measurements of optical overlay (OVL) of in-die targets(which better reflect the device overlay) may be enabled; (ii) Moreflexibility in design of target dummification is provided, e.g., byallowing the features to be parallel to the measurement direction; and(iii) The real estate and\or throughput specifications may be improved.

The present invention overcomes the limitation of the standardscatterometry overlay algorithm, which requires that the overlay betweentwo gratings is the only source of symmetry breaking. When additionalgratings are present, their relative offsets may vary the signal in away that cannot be treated using the standard overlay algorithm. Thiscontaminates the original two-layer overlay signal and results in aninaccurate measurement. Moreover, the machine learning algorithms enablereduction in the number of cell per target, extraction of moreinformation and/or faster extraction of information from themeasurements, and improved target designs, with relation to parametersof the machine learning algorithms. The machine learning algorithms maybe trained on target designs which are based on metrology simulations,to match a behavior of the target designs to a specified device patternsbehavior.

FIG. 1A is a high-level schematic overview illustration of multilayertargets and measurement methods thereof, according to some embodimentsof the invention. Prior art overlay targets and algorithms 90 relate totwo-layered targets and respective overlay algorithms, which use atleast two measurement cells having opposite predefined offsets ±f₀ alongeach measurement direction. It is emphasized that applying prior artalgorithms to targets with more than two layers results in an excessivenumber of variables due to the interaction between the illumination andthe target layers. For example, prior art algorithms provide twoequations (corresponding to the two target cells with opposite offsets)to derive two variables (the relative shifts between the layers, i.e.the overlay, and the coefficient A that related the overlay to thedifferential signal). In case of N>2 target layers, the two equationsprovided by prior art overlay algorithms are not sufficient to derivethe overlays between the N layers.

The present disclosure proposes methods 100 and overlay targets 290 withthree or more layers that enable extraction of various metrologyparameters, represented herein by overlays, from multi-layered targets.

Certain embodiments propose using modified overlay algorithms, as amethod 100A, to measure targets 201 having a plurality, N, of 2-celltargets, one cell pair for each of the N layers, all cells beingidentical but for a corresponding pair of cells for each layer, whichhave intended offsets ±f₀ along the measurement direction. For example,each target may comprise two periodic layers configured to measureoverlays between respective layers. The overlays may be derived frommeasurements of targets 201 by modified versions of prior art algorithms90 which take into account the intermediate layers. However, thisinnovative solution suffers from the following drawbacks: (i) thetargets are very different from the device (increasing thedevice-to-metrology bias) and (ii) the targets are less printable, evenon the scribe-line, due to their being farther away from the desiredprocess window. While it is possible to measure all overlays by modifiedSCOL algorithm 100A using targets 201, the following methods (100B-D)prove to be significantly more efficient in conserving real-estate andreducing MAM-time.

Machine learning algorithms 150 may be applied in association with anyof methods 100A-D to enhance derivation accuracy, reduce errors, speedup the overall measurement time, reduce the required number of cells 155and/or enable model-free on-the-fly metrology measurements ofdevice-like targets. In certain embodiments, machine learningalgorithm(s), may be trained on target designs which are based onmetrology simulations, to match a behavior of the target designs to aspecified device patterns' behavior.

Certain embodiments use a half of the number of cells, namely N cells(or more) for measuring N-layered targets by selecting the offsetsjudiciously, based on parameters of the machine learning algorithmsapplied to measurements of simulations. It is noted that disclosedmethods may be used to replace or enhance methods derived from analyticapproaches described in U.S. Patent Application Publication No.20160266505. The inventors have found out that the analytic approachensures the applicability and good convergence characteristics of themachine learning algorithms, while machine learning algorithms mayenable avoiding some of the assumptions involved in the analyticapproach.

Certain embodiments use even fewer cells, namely N−1 cells (or more) andpossibly even fewer cells than N−1, for measuring N-layered targets byselecting the offsets judiciously and utilizing pupil information(information in signals measured at the pupil plane with respect to thetarget of the metrology tool's optical system). It is noted that usingfewer than N cells per target is advantageous with respect toconservation of wafer real-estate and reduction of MAM(move-acquire-measure) time. It is noted that disclosed methods may beused to replace or enhance methods derived from analytic approachesdescribed in U.S. Patent Application Publication No. 20160266505. Theinventors have found out that the analytic approach ensures theapplicability and good convergence characteristics of the machinelearning algorithms, while machine learning algorithms may enableavoiding some of the assumptions involved in the analytic approach.Certain embodiments of the present invention comprise targets with n<2Ncells, which, by applying machine learning algorithm(s), enableextraction of overlay information from the n<2N cells. In certainembodiments, the targets comprise a single cell per target which, byapplying machine learning algorithm(s), enable model-free on-the-flyoptical overlay measurements of the single cell.

Certain embodiments use even fewer cells, namely as few as two cells (ormore) 300 set near devices for measuring N-layered targets by selectingthe offsets judiciously and utilizing pupil information, as well as byusing additional calibration targets 200 which may be positioned atregions farther away from the devices, e.g., on scribe lines, method100D, targets 200, 300 and FIG. 1B below (it is noted that method 100Duses and further develops methods 100B, 100C and targets 200, 300)—asexplained and exemplified in U.S. Patent Application Publication No.20160266505. It is noted that disclosed methods may be used to replaceor enhance methods derived from analytic approaches described in U.S.Patent Application Publication No. 20160266505. The inventors have foundout that the analytic approach ensures the applicability and goodconvergence characteristics of the machine learning algorithms, whilemachine learning algorithms may enable avoiding some of the assumptionsinvolved in the analytic approach.

In certain embodiments, the targets comprise a single cell per targetwhich, by applying machine learning algorithm(s), enable model-freeon-the-fly optical overlay measurements of the single cell.

Targets 290, 201, 200, 300 may be measured under different and possiblymultiple hardware and illumination configurations, e.g., using differentwavelengths and/or illumination modes, using different polarizations,using different apodizers or varying other elements in the opticalsystem, to enhance calibrations and measurements, especially underapplications of methods 100C and 100D. Machine learning algorithms 150may be applied in combination with or in place of any of methods 100A-D,possibly to further reduce the required number of cells 155.

FIG. 1B is a high-level schematic illustration of two types ofmultilayer targets 200, 300 and measurement methods 100 thereof,according to some embodiments of the invention. Machine learningalgorithms 150 may be enhance or replace first method 100B (that uses ananalysis of the differential signals from the multi-grating targets)and/or second method 100C (that uses an approximated decomposition ofthe overlay reported by the standard algorithm). Both methods rely onusing the full pupil information in order to extract the additionalneeded information. For each method, non-limiting examples forthree-layer targets are shown, with the calculations needed in order toinfer the overlay values. It is noted that the three-layer targets areused per measurement direction, i.e., with N=3 and two directionmeasurements X, Y, six cells are used to measure the overlays among thethree layers in both directions. Clearly, targets may be similarlydesigned to provide measurement along a single (critical direction)only. Machine learning algorithms 150 may replace or enhance methods100B, 100C, which are described in detail in U.S. Patent ApplicationPublication No. 20160266505. Machine learning algorithms 150 may furtherbe used to reduce the required number of cells 155.

The following describes methods 100B, 100C as two non-exhaustive andnon-limiting examples of method 100 of measuring overlay parameters inmultiplayer SCOL metrology targets, i.e., targets which employ aplurality of periodic structures (related to in the following, in anon-limiting manner as gratings), that are designed to be printed onwafers 60. Possible combinations of methods 100B and 100C are suggestedafter the principles of each method are explained. FIG. 2 is ahigh-level schematic illustration of multilayer targets 200, accordingto some embodiments of the invention. FIGS. 3A and 3B are high-levelschematic illustrations of multilayer targets 300, according to someembodiments of the invention.

FIG. 2 schematically illustrates target 200 comprising N cells 220 in Nlayers 210, each cell 220 having at least one periodic structure 230. Itis noted that the choice of an identical number (N) of cells and layersis made merely to simplify the explanation below, and does not limit thescope of the invention. The number of cells may be larger or smallerthan the number of layers as well (for the latter possibility seeadditional derivations below). Periodic structures 230 are overlapping(one above the other) characterized by predefined (intended) offsets(f_(i,n), relating to cell i and layer n) between cell layers 210, aswell as by uncontrolled (unintended) offsets (δ_(i,n), relating to celli and layer n) that are the aim of metrology method 100. Both offsetsare derived from signals 205 by estimation of the respective overlays,which are influence by both types of offsets. The measured signal infirst-order scatterometry overlay is the differential signal D 205,which is the intensity difference between the +1 and −1 diffractionorders when a target cell 220 is illuminated. Differential signal D 205is used as a non-limiting example, as the disclosed methods may be usedto measure differences between other diffraction orders as well asderived metrological measurements. The analytic approach to the measuredsignals provided in U.S. Patent Application Publication No. 20160266505may be replaced or complemented by the disclosed machine learningalgorithms, which may enable to avoid some of the assumptions involvedin the analytic approach, while their convergence is suggest or possiblyaided by the analytic approaches. Machine learning algorithms 150 mayfurther be used to reduce the required number of cells 155.

Standard overlay targets have two layers and therefore two unknownparameters, and the two cells are used to provide the required twomeasured signals. Method 100 comprises the development of a newformalism that is required to handle more than two overlapping gratings,in order to distinguish between the effects of the offsets of thedifferent layers on the signal. The inventors note that such challengehas not yet been handled due to the high-level of complexity involved inthe design of the targets, in the theoretical analysis and in thepractical measurement procedures, all of which are disclosed in thepresent invention. In the next sections two innovative formalisms(corresponding to procedures 100B and 100C) are described anddemonstrated by simplified, non-limiting models. The inventors note thata person skilled in the art can easily expand these models to includeadditional contributions, e.g., higher diffraction orders, which arethus considered likewise a part of the present disclosure.

Method 100B uses the inspection of differential signals 205 from targets200 to obtain overlays 235 (notated—OVL). Two variants of method 100Bare presented—one assuming that the values of the previous OVLs areknown, and a more advance variant that uses the pupil information inorder to obtain all OVL values in the design, without a priori knowledgeof the OVLs.

The second, more advanced variant of method 100, related to herein asmethod 100C, overcomes the need for all previous OVL values by combiningthe information from all the pixels within the pupil and using the factthat the previous OVL values do not depend on the pixel position. Thelatter observation is used to define a cost function Ω which has a zerodifferential with respect to any OVL value (the example is with respectto OVL₁, in a non-limiting manner) as expressed and developed in U.S.Patent Application Publication No. 20160266505. Machine learningalgorithms 150 may be applied in combination with or in place methods100B and/or 100C, to enhance derivation accuracy, reduce errors, speedup the overall measurement time, reduce the required number of cells 155and/or enable model-free on-the-fly metrology measurements ofdevice-like targets.

FIGS. 3A and 3B are high-level schematic illustrations of multilayertargets 300, according to some embodiments of the invention. FIGS. 3Aand 3B illustrate in a non-limiting manner three-layered target 300, thedisclosed principles may be implemented to multi-layered targets by aperson skilled in the art, hence the latter are considered likewise partof the disclosed invention.

Multilayer targets are not used in the prior art since the additionallayers (beyond two) are an additional symmetry breaking source whichcontaminates the overlay signal from the two layers and results ininaccurate measurement. In the following, the one or more additionallayer(s) are treated as inaccuracy source and their effect on the signalis characterized. The characterization is used (i) to eliminate theinaccuracy contribution of the additional layer(s) to the overlay of anoriginal two-layered target (which may be selected arbitrarily in target300); and (ii) to calculate the offset of the additional layer(s) withrespect to the original layers. These are part of method 100D (see FIG.1A), which may replace and/or augment methods 100B and 100C of measuringmultilayered targets 200, which was described above. Particularly, thedistinction between targets 200 and 300 is made merely to clarify theexplanations and not to limit the scope of the invention, as clearlymulti-layered targets may be design to combine the features of targets200 and 300.

FIG. 3A schematically illustrates a non-limiting case, in which top andintermediate layers 310, 320, respectively, are regarded as the originallayers for which an overlay is to be calculated, while bottom layer 330(which may be replaced by multiple layers) is regarded as the inaccuracysource. The effect of the bottom layer offset with respect to theintermediate layer is similar to a symmetry breaking due to side wallangle asymmetry. FIG. 3B schematically illustrates three layered target300 with designation of the refracted electric fields as defined below,upon illumination I. It is assumed for the sake of simplicity, in anon-limiting manner, that the periodic structures in layers 310, 320 330are parallel gratings with identical pitches. It is further assumed, ina non-limiting manner, that the leading orders of the refracted electricfield are E₁ ^(r)—the first order signal refracted off top grating 310,E₀₁₀—the field transmitted through top grating 210, refracted to thefirst order off middle grating 320, and transmitted through top grating310, and E₀₀₁₀₀—the field transmitted through top and middle gratings210, 220 respectively, refracted to the first order off bottom grating330, and transmitted through middle and top gratings 320, 310,respectively, as illustrated in FIG. 3B. The corresponding intensity ofeach of these fields as I₁ ^(r)=|E₁ ^(r)|², I₀₁₀=|E₀₁₀|², and |E₀₀₁₀₀|².

The analytic approach to the measured signals provided in U.S. PatentApplication Publication No. 20160266505, under the assumptions statedabove, may be replaced or complemented by the disclosed machine learningalgorithms, which may enable to avoid some of the assumptions involvedin the analytic approach, while their convergence is suggest or possiblyaided by the analytic approaches. For example, the analytic approach ofU.S. Patent Application Publication No. 20160266505 illustrates that themeasured overlay per pixel can be separated into the overlay between thetop layers and a term that depends on the bottom layer offset. inanother example, the analytic approach separates the variables momentumdependency and bottom-layer offset. Such indications may support theapplication of the machine learning algorithms and help identify usefulparameters of the machine learning algorithms, to be used as input formeasurement and target design methods.

The calibration may be applied to the full sampling or to the nextwafers. Alternatively or complementarily, the differential signalanalysis (methods 100B, 100C, possibly enhanced by machine learningalgorithms) may be applied to the calibration targets. (iii) Subsamplingmay be measured across the wafers at dedicated targets positioned nextto external reference targets and the matching between the targets maybe optimized. The calibration may be applied to the full sampling or tothe next wafers. (iv) A principal-component analysis (PCA) may beperformed on the subsamples to give the relative measure of {tilde over(f)}(k), and the absolute value can be calculated since the OVL isobtained from the multi-cell targets, as described above. In certainembodiments, machine learning algorithms 150 may be used in relation tothe PCA, e.g. before the PCA, after the PCA, using principal componentsof the PCA for the machine learning algorithms and/or using machinelearning algorithms possibly to improve the derivation of the principalcomponents.

Returning to FIG. 1B, certain embodiments of method 100D may combine theuse of methods 100B and 100C. For example, two-cell targets 300 may beprinted on the wafer, together with a smaller number of (dedicated,calibration) multi-cell targets 200 (i.e., targets having three cells ormore). Multi-cell targets 200 may be sampled in order to obtain the{tilde over (f)}(k) function, either by the overlay decompositionmethod, or by inspecting the resulting differential signals andobtaining {tilde over (f)}(k) from differential signal analysis method100B. Machine learning algorithms 150 may be applied in association withof method 100D described below and in U.S. Patent ApplicationPublication No. 20160266505, to enhance derivation accuracy, reduceerrors, speed up the overall measurement time, reduce the requirednumber of cells 155 and/or enable model-free on-the-fly metrologymeasurements of device-like targets. In certain embodiments, machinelearning algorithm(s), may be trained on target designs which are basedon metrology simulations, to match a behavior of the target designs to aspecified device patterns' behavior.

It is noted that the assumption of three periodic structures (gratings)is a non-limiting one, presented here for simplification purposes only.In case of two (or more) measurement directions, respective periodicstructures may be added. In order to conserve wafer real-estate,calibration multi-cell targets 200 may be relative few whilemeasurements of two-cell targets 300 may be carried out using thecalibration derived therefrom.

More specifically, by studying the OVL distribution across the pupil ina linearity array of the bottom grating, {tilde over (f)}(k) may beextracted (for example—using principal component analysis and/or machinelearning algorithms 150). For all other sites on the wafer the “OVL” canbe separated into OVL₂₃ (“common to all pixels”) and OVL₁₂ (“per pixelinaccuracy”), as described in detail in U.S. Patent ApplicationPublication No. 20160266505, enabling dedicated multi-layered targetmeasurements using two cells (of three layers). Respective metrologymeasurements of any of targets 290, 201, 200, 300 and application ofmachine learning algorithms 150 thereto are also considered part of thepresent disclosure.

FIG. 4 is a high-level flowchart illustrating method 100, according tosome embodiments of the invention. Method 100 may be carried out atleast partially by at least one computer processor. Computer programproducts and corresponding metrology modules are provided, whichcomprise a computer readable storage medium having computer readableprogram embodied therewith and configured to carry out method 100 atleast partially. Target design files as well as metrology measurementsof the targets are also provided.

Method 100 may comprise any of the following, separate or combined:modifying current OVL algorithms to operate on N cell pairs (2N cells),each pair with opposite offsets in one layer (method 100A) (e.g., eachtarget with two periodic layers configured to measure overlays betweenrespective layers); using only N cells, designed with specific intendedoffsets that enable derivation of the overlay of the measurements(method 100B); using pupil information to reduce the number of requiredcells to N−1 (method 100C), and using calibration targets to reduce thenumber of specific overlay targets below N−1, possibly down to 2 perN-layered target (method 100D).

Method 100 may further comprise enhancing and/or replacing any of theanalytic methods (100A-100D) by machine learning algorithm(s) 150 (stage102). In certain embodiments, method 100 may comprise applying machinelearning algorithm(s) to calibrate overlay sensitivity and possibly toreduce the number of cells below 2N (stage 105).

Method 100 may comprise configuring a multi-layered metrology target tohave a plurality, M, of target cells over at least three, N≤M, targetlayers, each cell having at least one periodic structure in each layer(stage 110) and configuring the periodic structures of each cell to beoffset with respect to each other by specified offsets (stage 115).Method 100 may comprise measuring, scatterometrically, at least Mdifferential signals from the multi-layered metrology target (stage120), and applying machine learning algorithm(s) to calculate SCOLmetrology parameters from the M measurements of the multi-layeredmetrology target by relating the SCOL metrology parameters to thedifferential signals and to the specified offsets (stage 130). The SCOLmetrology parameters may be overlays between the N layers. Applyingmachine learning algorithm(s) 130 to calculate the SCOL metrologyparameters may be carried out sequentially for consecutive layers (stage132), e.g., as in first variant 100B of method 100 described above. Forexample, the SCOL metrology parameters may be overlays between the Nlayers, and the machine learning algorithm(s) may be applied in relationto the analytic model described in U.S. Patent Application PublicationNo. 20160266505.

Alternatively or complementarily, applying machine learning algorithm(s)130 to calculate the SCOL metrology parameters may be carried outsimultaneously for the layers (stage 135), e.g., as in second variant100C of method 100 described above, by carrying out the measuring at apupil plane with respect to the target (stage 137) and usingmeasurements of a plurality of pixel positions at the pupil plane (stage138). For example, the SCOL metrology parameters may be overlays betweenthe N layers, and the machine learning algorithm(s) may be applied inrelation to the analytic model described in U.S. Patent ApplicationPublication No. 20160266505. In a non-limiting example, N=3, the SCOLmetrology parameters are overlays between the three layers, and themachine learning algorithm(s) may be applied in relation to the analyticmodel described in U.S. Patent Application Publication No. 20160266505.In certain embodiments, any of the measurements may be carried out inthe field plane (stage 139).

In certain embodiments, method 100 may comprise deriving the machinelearning algorithm(s) (e.g., stages 120 and/or 130) during setup ortraining, e.g., once, and applying the derived algorithm(s) (e.g.,stages 135) in runtime (stage 140), possibly adjusting the derivedalgorithms if needed. Certain embodiments comprise using, at leastpartly, simulations for the derivation of the machine learningalgorithm(s) (stage 142).

Certain embodiments comprise multi-layered metrology targets having aplurality of target cells over at least three target layers, each cellhaving at least one periodic structure in each layer, with the periodicstructures of each cell being offset with respect to each other byspecified offsets. The targets may provide SCOL measurements which arelikewise part of the present disclosure.

Method 100 provides multiple novel aspects, such as: The measurement ofSCOL targets with more than two overlapping parallel gratings withminimal inaccuracy penalty; Combination of two-cell and multiple-celltargets sampling for accurate measurements of multiple overlappinggrating targets; Targets and measurement methods which follow all in-diedevice layout restrictions including lateral and vertical constrainswith no inaccuracy penalty; Multi-cell multi-grating targets andmeasurement methods for improved throughput and\or real estate; Targetdesign optimization based on simulations taking into account all processand lithography steps and resultant patterns, rather than only the twospecific desired layers; Optimization of all layer patterns based on ananalytic model—the model predicts the optimal optical propertiesminimizing undesired contributions to the signal; Use of informationacross the pupil such as reflectivity, differential signals and\oroverlay in order to get the accurate overlay per layer; the combinationof the aforementioned per-pixel response with multi-cell overlaycalculation in order to obtain a calibration for the standard algorithmsuch that the desired overlay can be obtained from a two-cell target;and application of machine learning algorithms to any of the above.

Quasiperiodic Targets

In the following, examples of quasiperiodic SCOL targets are presented,which are more similar to device patterns and are periodic at predefinedscales, but are not lattices. This means that the full structure cannotbe divided into smaller identical structures (unit cells). It is notedbelow, that actual device patterns may also, under certain circumstanceswhich are described below, considered quasiperiodic SCOL targets, and asshown below, do not have to include the intended shifts. Hence thefollowing disclosure enables measuring overlay of certain device designsdirectly, in spite of their non-periodicity.

Machine learning algorithms may be applied on the measurements of thedisclosed targets to enhance results derivation accuracy, reduce errors,speed up the overall measurement time, reduce the required number ofcells and/or enable model-free on-the-fly metrology measurements ofdevice-like targets. In certain embodiments, machine learningalgorithm(s), may be trained on target designs which are based onmetrology simulations, to match a behavior of the target designs to aspecified device patterns' behavior.

FIGS. 5A-5D and 6A-6F are high-level schematic illustrations ofquasiperiodic SCOL targets 400, according to some embodiments of theinvention. Targets 400 exemplify targets which fulfill the requirementsof various OVL/alignment techniques (e.g., SCOL, AIM, scanner alignmentmarks) for periodic target, yet are not periodic in the sense thattargets 400 and do not have a unit cell. While targets 400 have norepeating unit cell, the Fourier transform of targets 400 does revealperiodicity at some defined length scales corresponding to an effectivetarget pitch at an additional length scale, which may be much biggerthan the actual fine scale pitch. In the analysis of the measurements oftargets 400, the random parts which break the translation symmetry maybe treated as “noise” and may be averaged out using measurements oflarge areas or using multiple measurements of different target areas.Alternatively or additionally, measurement conditions may be chosen tominimize the contribution of translation-variant features, or theircontribution may be eliminated using sophisticated signal analysis,target design and\or hardware, as exemplified below. Respectivemetrology measurements of targets 400 are also considered part of thepresent disclosure.

In FIG. 5A, target 400 is illustrated schematically using a basicpattern of horizontal and vertical lines to indicate the quasi-periodicnature of target 400 (X and Y axes illustrate two measurement axes, theperiodicity along the Y axis may correspond to the pitch in standardSCOL targets). It is noted that in the details, elements 410-410F etc.of which target 400 is composed, each includes gaps and cuts whichmodify the elements and target 400 as a whole from being a regular grid,making target 400 grid-based but incorporating multiple irregularitiesthat are derived from device patterns as illustrated in FIGS. 5B and 5Cin an exemplary manner. FIGS. 6A-6F further elaborate on this aspect bydenoting target 400 as being made of blocks 410A-410F etc. (FIG. 6A)which are designed as schematic representations or abstractions ofdevice patterns (FIGS. 6B-6E) and may be combined to form quasi-periodictargets 400 (FIG. 6F). It is emphasized that all block designs are basedon a similar periodicity that is represented by the respective grids,yet include multiple irregularities or aberrations from the gridsymmetry, which overall yield quasi-periodic target 400.

FIG. 5B is a high-level schematic illustration of a simplified devicelayout 420 (e.g., a NAND gate layout) as a basis for pattern 410Aexemplified in FIG. 5C. Pattern 410A comprise specified features, e.g.,features that may be derived from device layout 420 by furthersimplification, such as maintaining rows 411 from device layout 420 andusing various types of cuts 412 (analogous to metal lines connecting therows in the actual device layout) to yield pattern 410A as well asalternative patterns such as pattern 410E illustrated in FIG. 5D. FIG.5A schematically illustrates a combination of a plurality of patternsdenoted by 410A, 410B, 410C, 410D, 410E, 410F etc. which may likewise bevariations on the dimensions of pattern 410A and/or on the configurationof cuts 412 in it. Target 400 may thus be described as superposition ofrepeating unit cell and varying cuts, and/or may be designed to lack adefined repeating unit cell altogether. Different cuts in patterns410A-F may be selected to represent different logic gates. Theillustrated design may be provided for one or more layers of target 400.It is noted that the illustrated lines and cuts may be the result ofmultiple lithography steps (e.g., possibly creating the lines with pitchmultiplication processes) and then possibly multiple applications ofcuts. The patterns may however also be carried out in a singlelithography step. The illustrated lines and cuts serve to provide anon-limiting example for specified features of the patterns, and may bereplaced with other features with respect to device design.

FIG. 6A illustrates schematically targets 400 as quasi-periodic in thesense that they exhibit periodicity along the Y axis that results fromthe general organization of the wafer (Y direction periodicity, possiblyin the order of magnitude of the pitches of prior art targets) and aregularity along the X axis which results from the design principles ofthe wafer yet is not strictly periodic as designs 410A-410F etc. may notbe periodic, and designs 410A-410F etc. may be alternatednon-periodically. An evaluation of the degree of irregularity in thedesign of target 400 is presented below, and shown to still enablederivation of metrology signals and metrology parameters while takinginto account the deviations introduced by the irregularities.

An important and surprising insight the inventors gain from thedisclosed analysis is that devices and device sections may also beconsidered as quasi-periodic targets 400 and hence directly measuredusing metrology techniques and algorithms presented herein, underconsideration of the effects introduced by their “irregularities” asconsidered with respect to strict periodicity. Moreover, thequasi-periodic nature of targets 400 enhances the applicability andefficiency of machine learning algorithms to measurements of targets400.

FIG. 6B schematically illustrates schemes 420B, 420C that representschematic layouts of NAND and NOT gates respectively (the backgroundgrid serves merely to illustrate the periodicity of the pattern and isnot an actual part of the pattern). In this exemplary process the M1pattern is produced using three lithography steps (denoted LELELE with Lstanding for a lithography step and E standing for an etch step, thethree steps applied to the same physical layer) to give thecorresponding M1 a, M1 b and M1 c. FIG. 6C schematically illustratesonly the M1 pattern which is common to 420B and 420C. Pattern 410C thatmay be used to represent designs 420B, 420C in target 400. FIG. 6Dschematically illustrates schemes 420D, 420E that represent schematiclayouts of OR and AND gates respectively and FIG. 6E schematicallyillustrates corresponding M1 patterns 410B, 410D that may be used torepresent designs 420D, 420E in target 400. Clearly, additional patternsmay be used and integrated into target 400 according to variousperformance requirements and optimizations. It is noted that allillustrated designs 410A-410E illustrate the quasi-periodic nature oftargets 400 which maintain a large degree of periodicity whileintroducing irregularities in the patterns that correspond to specificdevice designs. FIG. 6F schematically illustrates a combination ofpatterns 420B, 420C, 420D that yields quasi-periodic target 400 (thebackground grid serves merely to illustrate the periodicity of thetarget and is not an actual part of the target). It is noted thatschemes 420A-E are used as a schematic adaptation of circuits such asthose presented by U.S. Pat. No. 8,863,063 and they serve asnon-limiting examples for possible schemes which may be used to derivecorresponding patterns 410A-F and other patterns.

FIGS. 7A and 7B present simulation results of the effect of the noiseintroduced by the non-periodic target design on the first orderamplitude, according to some embodiments of the invention. The noiserepresents irregularities in an essentially periodic structure, whichwas termed quasi-periodicity above. The following diagrams may be usedto estimate to what extent the deviations from periodicity, which wereexemplified in FIGS. 5A-5D and 6A-6F, degrade the metrology signalsderived from target 400. The pupil plane signal (amplitude) of a gratingwith irregularities was calculated using Fraunhofer approximation. InOVL measurement, modification dS in the first order noise roughlychanges the OVL by dS/A (A being the measurement sensitivity). Randomnoise was added to the grating in form of locations in which theamplitude was modified to zero. The random noise may be understood torepresent irregularities due to differences that arise from specificpatterns 410A-410F. The calculation was repeated for several differentillumination beam locations. The effect of the noise magnitude wascalculated for the first diffraction order amplitude distribution as afunction of beam location. In FIG. 7A the error bars indicate thestandard deviation of ten different beam locations. All values arenormalized with respect to the unperturbed intensity. FIG. 7B showsexplicitly the variability between the first order intensity whendifferent locations were sampled (corresponding to the error bars ofFIG. 7A). Different spatial distributions of the noisy points create anuncertainty of ca. 0.3% in the amplitude for a noise magnitude of 2%.FIGS. 7A and 7B illustrate that the deviation from strict periodicityresult in a controllable noise that represents the irregularities in thetarget design, and may be taken into account as an inaccuracy factorwhen deriving metrology results from targets 400. Moreover, FIGS. 7A and7B provide tools for handling noise due to irregularities in targetstructures, or in device designs which are used as targets, as suggestedbelow. Machine learning algorithms may be configured with respect tosuch results and tools to converge quickly and provide accuratederivations.

This inaccuracy may be treated either algorithmically (for example usingknown symmetry properties of the signal) or by selecting measurementpoints which cancel out the symmetry breaking. The latter can be donefor example by automatic analysis of the reticle. Machine learningalgorithms may be applied to enhance derivation accuracy, reduce errors,speed up the overall measurement time, reduce the required number ofcells and/or enable model-free on-the-fly metrology measurements ofdevice-like targets. In certain embodiments, machine learningalgorithm(s), may be trained on target designs which are based onmetrology simulations, to match a behavior of the target designs to aspecified device patterns' behavior.

Certain embodiments comprise metrology targets 400 having irregularlyrepeating units 410A-F along at least one direction of target 400(possibly two perpendicular directions), wherein the units comprisedevice-like patterns having one or more (different) sets of lines andcuts, which are derived from respective device designs. For example,unit lengths, characteristics of lines in the unit and/orcharacteristics of cuts in the unit may be varied along the at least onedirection of target 400. Target 400 may comprise two or more layers andmay provide SCOL measurements which are likewise part of the presentdisclosure.

FIG. 8 is a high-level flowchart illustrating method 600, according tosome embodiments of the invention. Method 600 may be carried out atleast partially by at least one computer processor. Computer programproducts and corresponding metrology modules are provided, whichcomprise a computer readable storage medium having computer readableprogram embodied therewith and configured to carry out method 600 atleast partially. Target design files as well as metrology measurementsof the targets are also provided.

Method 600 may comprise deriving a plurality of device-like patternsfrom a respective plurality of device designs, wherein device-likepatterns comprise different sets of lines and cuts as exemplaryspecified pattern features (stage 615), and designing a metrology targetusing the derived device-like patterns as irregularly repeating unitsalong at least one direction of the target (stage 620).

Method 600 may comprise varying along the at least one direction of thetarget at least one of: a unit length, characteristics of lines in theunit and characteristics of cuts in the unit (stage 630). The at leastone direction may comprise two perpendicular directions of the target.The target may comprise at least two layers. Method 600 may compriseestimating, possibly using machine learning algorithms, a noiseresulting from the target irregularities (stage 632), being thedeviations from strict periodicity, and designing or selectingappropriate patterns according to specified noise thresholds (stage634). Method 600 may comprise estimating, possibly using machinelearning algorithms, a measurement error according to the estimatednoise (stage 636). Method 600 may comprise utilizing pattern symmetryproperties to estimate and improve the signals received therefrom (stage638), as explained above (e.g., by treating the estimated noisealgorithmically, using known symmetry properties of the signal and/or byselecting measurement points which cancel out the symmetry breaking,e.g., by automatic analysis of the reticle). Method 600 may comprise,e.g., following estimation 632, learning the overlay signal usingreference measurements in order to reduce the signal contamination dueto the target irregularities (stage 639), e.g., by identifying andreducing the signal contamination due to target irregularities from theoverlay signal, using the machine learning algorithm(s).

In certain embodiments, method 600 may comprise deriving the machinelearning algorithm(s) during setup or training, e.g., once, and applyingthe derived algorithm(s) in runtime (stage 640), possibly adjusting thederived algorithms if needed. Certain embodiments comprise using, atleast partly, simulations for the derivation of the machine learningalgorithm(s) (stage 642).

Avoiding Offsets in Device Targets

Returning to the basic SCOL assumption, it is assumed in U.S. PatentApplication Publication No. 20160266505 that the measured differentialsignal (intensity difference between the first and respective minusfirst order) can be written as D(n)=A(n)·ε(n), with n being an index forthe target cell (or target site) and ε being the lateral offset betweenthe two target periodic structures (e.g., gratings) in the measurementdirection. Since both the sensitivity A and the relative offset (or OVL)may change between targets, both parameters should be calculated pertarget, and thus two measurements with the same sensitivity and OVL arerequired. If the sensitivity does not change, the OVL can be calculatedusing OVL(n)=D(n)/A, but in reality this does not hold results in biginaccuracy values.

In order to create two informative measurements, predetermined offsetsare applied by design. These offsets may damage the electricalproperties of the device and therefore cannot be applied on realdevices. Since for many OVL alignments there is only one criticaldirection, as illustrated schematically in FIGS. 9 and 10 below, offsetsin the orthogonal direction may be applied without damaging the deviceand without affecting the final (after etch) pattern. In conventionalSCOL algorithms, offsets in the orthogonal direction do not help recoverthe sensitivity because the device pattern is not symmetric for rotationof 90° and, as a consequence, the measurement of the sensitivity in thisdirection does not necessarily correlate with the desired sensitivity.In the following derivations, linear approximation for the differentialsignal is used for simplicity, in a non-limiting manner. It isexplicitly stated, that all methods disclosed below are valid also usinghigher order approximations for the differential signal and suchapplications are considered part of the present disclosure. Thefollowing methods use orthogonal offsets information to derive metrologyparameters such as overlay, using orthogonal diffraction orders of thesame target which do not require any offsets and using orthogonaltarget(s) with a different design, having offsets in a non-criticaldirection, both options enabling potential use of actual devices astargets.

FIGS. 9 and 10 are high-level schematic illustrations of devicealignments 97, according to some embodiments of the invention. Forexample, FIGS. 9 and 10 may represent an alignment of contacts 711 togates 712. FIGS. 9 and 10 schematically illustrate that the alignmentalong one direction (critical direction, denoted X) imposes muchstricter overlay requirements (smaller OVL values) than the alignmentalong the perpendicular direction (non-critical direction, denoted Y).FIG. 10 also illustrates schematically the first diffraction ordersignals 98 in the pupil plane (pupil image, for an exemplary centralillumination) of device 97, with +1 and −1 diffraction signals along theY direction (the direction perpendicular to the critical direction)being similar to each other (and having rotational symmetry) while the+1 and −1 diffraction signals along the X direction (the criticaldirection) differ from each other, e.g., in intensity due to overlaysymmetry breaking by elements 711.

In the following, the rotational symmetry along the non-criticalmeasurement axis is utilized to enable measurement along the criticalmeasurement axis without the present necessity to introduce designedoffsets along the critical measurement axis. Moreover, machine learningalgorithms may be applied to the measurements to enhance derivationaccuracy, reduce errors, speed up the overall measurement time, reducethe required number of cells and/or enable model-free on-the-flymetrology measurements of device-like targets. In certain embodiments,machine learning algorithm(s), may be trained on target designs whichare based on metrology simulations, to match a behavior of the targetdesigns to a specified device patterns' behavior.

FIG. 11 is a high-level schematic illustration of leading diffractionorders along the non-critical and critical measurement directions, 715,716 respectively, according to some embodiments of the invention. FIG.11 illustrates a simplified model for a grating over grating at the twodirections (represented as grating over grating model 725 and singlegrating model 726, as along the non-critical measurement directions thetop grating is non-periodic, see FIG. 10).

It is noted that the simplified model is presented for explanatoryreasons, and does not limit the invention. Gratings 701, 703 are torepresent any periodic structure, and equivalent models may be used formulti-layered periodic structures. Moreover, the measured structures maybe metrology targets and/or actual devices. For example, model 725 maybe seen as representing effectively two-dimensional periodic structureswhile models 726 may be seen as representing effectively one-dimensionalperiodic structures.

Machine learning algorithms may be configured to improve results withrespect to any such model (e.g., models 725, 726) and may possibly beconfigured to enhance or replace the use of these models. Results basedon models 725, 726, may be used to guide or train the machine learningalgorithms and also suggest the applicability and good convergencecharacteristics of the machine learning algorithms, while machinelearning algorithms may enable avoiding some of the assumptions involvedin the models.

In model 725, the electric field on the collection pupil in the Xdirection is the interference between the two diffraction modesillustrated in FIG. 11 and represented in equations presented anddeveloped in U.S. Patent Application Publication No. 20160266505, whichmay be complemented or replaced by the machine learning algorithms. Inmodel 726, the electric field E and the resulting measured intensityI_(p) are expressed as component 77 reflected off lower grating 703,after passing through intermediate layer 702 and upper layer 701, thelatter including the upper grating along the non-critical (non-measured)direction, and hence lacking periodicity, and are described analyticallyin U.S. Patent Application Publication No. 20160266505, which may becomplemented or replaced by the machine learning algorithms.

As stated above, more complex models and calibration functions may beimplemented using the same methodology and are considered part of thepresent disclosure. It is noted that the orthogonal diffraction ordermay also be used for calculation of geometrical properties of the target(for example: Critical Dimensions), with or without optical modeling.Machine learning algorithms may be configured to improve results withrespect to any such model and may possibly be configured to enhance orreplace the use of these models. Results based on these models may beused to guide or train the machine learning algorithms, and possiblyavoid some of the assumptions involved in the models.

The disclosed analytic and machine learning methods (see also method 800below) may be implemented in various ways to derive metrologymeasurements (of which the overlay was presented as non-limitingexample) from various device and target designs. As a non-limitingexample, FIG. 12 schematically illustrates one exemplary configurationfor the application of the method.

FIG. 12 is a high-level schematic illustration of a target 700,incorporating an offset-less device portion, according to someembodiments of the invention. Target 700 may be designed to provide asensitivity calculation without introduction of offsets along criticalOVL dimension 715 (in cell 710), by using additional cell(s) 720 withpattern and offsets in different direction(s) 716 (e.g., perpendicularto the critical direction of cell 710). FIG. 12 illustrates in anon-limiting manner a three-cells designs in two layers, yet may beextended to a multi-layered design as explained above in the presentdisclosure, as well to quasi-periodic targets and devices as explainedabove. Target 700 enables overlay calculation along critical direction715 without the need to introduce intended offsets in this direction. Itis explicitly noted that cell 710 may be understood as representing atleast a portion of an actual device design, the disclosed method thusenabling measuring devices directly, without introducing offsets atleast along the critical direction of the device, possibly withoutintroducing any offsets into the device design.

U.S. Patent Application Publication No. 20160266505 presents analyticalmethods applicable to measurements of central cell 710 for the Ydifferential signal calculation and of other (e.g., two) cells 720 withintended offsets ±f₀, providing equations which express the sensitivityA of one target as being approximated by a function of a second nearbytarget. Machine learning algorithms may be used to enhance or replacesuch calculations, possibly to achieve any of reduction in the number ofcell per target, extraction of more information and/or faster extractionof information from the measurements, and improved target designs, withrelation to parameters of the machine learning algorithms. The machinelearning algorithms may be trained on target designs which are based onmetrology simulations, to match a behavior of the target designs to aspecified device patterns behavior.

FIG. 13 presents a table with exemplary simulation results of theresulting sensitivity values for different combinations of first andsecond cells designs 710 and 720 respectively, according to someembodiments of the invention. The table presents the sensitivity asinaccuracy values (in nm, based on simulations), high sensitivitiesbeing above ca. 1 nm and low sensitivities below ca. 1 nm, anddemonstrates the effectivity of the disclosed method. Method 800 wastested on fifteen different target designs and ninety processvariations. The presented calculations, introduced in U.S. PatentApplication Publication No. 20160266505, ensure the applicability andgood convergence characteristics of the machine learning algorithms,while machine learning algorithms may enable improving results andpossibly avoiding some of the assumptions involved in the analyticapproach.

The number of cells in SCOL targets may be reduced using the methodsdescribed above. For example, relative offsets of N features in the samelayer may be measured using N+1 cells instead of 2N cells—the firstfeature sensitivity and OVL are calculated using two cells, and allother designs may have a single cell for which the calibrationsensitivity function may be used with respect to the first design. Incertain embodiments, machine learning algorithms may be used to furtherreduce the number of cells, possibly even to single cell per target,with the machine learning algorithms configured to enable model-freeon-the-fly optical overlay measurements of the single cell targets.Metrology procedures may also be improved, as after calculating thecalibration function based on simulations, measurements and\or machinelearning algorithms, the run sequence may comprise on-the-flymeasurements of single cells and use of the orthogonal direction signalto calibrate the sensitivity, using the calculated calibration function.It is noted that in case of unstable processes, several differentcalibration targets may be used.

FIG. 14 is a high-level flowchart illustrating a method 800 of measuringoverlays without introducing intended shift along critical directions,according to some embodiments of the invention. Method 800 may beimplemented at least partly using machine learning algorithms. Method800 may be at least partly implemented by at least one computerprocessor, e.g., in a metrology module. Certain embodiments comprisecomputer program products comprising a computer readable storage mediumhaving computer readable program embodied therewith and configured tocarry out of the relevant stages of method 800. Certain embodimentscomprise target design files of respective targets designed byembodiments of method 800.

Method 800 comprises measuring overlay(s) while avoiding prior artintroduction of intended offset(s) along a critical measurementdirection in at least one of the target cells (stage 805). The measuringmay be carried out in a model-free manner, possibly using machinelearning algorithms. Method 800 may comprise applying machine learningalgorithms to calibrate sensitivity parameter(s) using offsets in anorthogonal, non-critical measurement direction (stage 810), using theintensity of diffraction orders orthogonal to the critical measurementdirection. Alternatively or complementarily, method 800 may comprisedesigning reference calibration targets on scribe lines (stage 820) andapplying machine learning algorithms to calibrate sensitivityparameter(s) using offsets in calibration target(s) (stage 825). Method800 may comprise selecting parameters of reference targets to reduceinaccuracy according to the model (stage 830). Method 800 may compriseusing at least one additional target cell other than the at least onetarget cell to measure sensitivities (stage 815), e.g., by introducedoffsets (along either or both critical and non-critical directions). Theat least one additional target cell may be adjacent to the targetcell(s) and/or be configured as separate calibration targets.

Method 800 may comprise designing metrology targets that incorporate atleast a part of a device design, with cells having offsets at anon-critical direction while the device part exhibits no offsets (stage840). In certain embodiments, multiple parts of an actual device may beused and combined into a single metrology measurement, method 800further comprising selecting multiple device design portions to yield aderived pupil plane image from respective pupil images of the portions,which satisfies a specified criterion (e.g., with respect to periodicityand/or the estimated noise) (stage 845). For example, a pupil image usedin the OVL calculation may be an average of few pupil images measured atdifferent, possibly disparate device areas 50A. The selection of thecombination may be pre-defined or be carried out automatically in orderto effectively select the signal that provide certain characteristics,e.g., being most similar to a signal derived from a periodic target,exhibiting the lowest level of noise etc. Method 800 may comprise usingmachine learning algorithms to optimize target design according to theapproximation assumptions (stage 850), e.g., by using machine learningalgorithms to reduce variation in shape factors of the periodicstructures (stage 855). Offsets may be introduced along the orthogonal,non-critical measurement direction of the device design and/or inadjacent, non-device additional cells and/or in calibration cells, e.g.,on scribe lines.

The following aspects are provided by method 800 and the disclosureabove. OVL sensitivity calibration may be carried out based onon-the-fly information from additional diffraction orders, which mayinclude orthogonal diffraction orders and possibly derived by machinelearning algorithms. OVL sensitivity calibration may be carried outbased on on-the-fly information from a second target with a differentdesign and/or from additional targets with different designs and\ordiffraction orders reflectivity. These enable using OVL targets with nooffsets in the critical measurement direction (enabling direct devicemeasurements with no degradation in electrical functionality) accordingto the disclosed measurement methodology. Examples for target which werepresented above include any of: a one-cell SCOL target for one direction(x or y) in which the sensitivity is calculated based on orthogonaldirection reflectivity; a three-cell SCOL target composed of twostandard cells for the first direction and a third cell for theorthogonal direction; a SCOL target for N designs along a singledirection containing N+1 cells (instead of 2N) cells—a first design hastwo cells and all others have a single cell per design; and a SCOLtarget for N designs along a critical direction containing N+2 cells(instead of 2N) cells—two cells in the orthogonal non-critical directionand all others single cell without offsets per design. It is importantto note that all the cells must not be adjacent to each other, forexample the target may be a combination of cells located within thedevice active area and cells in its periphery.

The disclosure further provides OVL model-free measurements of targetswith at least one of: No intended offsets; no defined unit cell;multiple (more than two) overlapping patterning (i.e., differentlithography steps, possibly applied to the same physical layer); andmeasurement of device patterns using SCOL like algorithm (run timemodel-free approach). The methods enable optical measurements of devicepatterns after resist development as well as optical measurements offinal and after etch device patterns. The disclosure further providesmetrology-simulations-based target design optimization to match specificdevice patterns behavior as well as metrology simulations combined withlithography and\or process simulations for target design optimization tomatch specific device patterns behavior. Finally, model-free on-the-flyoptical OVL measurements using a single cell and combined OVL and OCD(optical critical dimension) targets with model-free OVL algorithm areprovided (possibly requiring measurements using multiple hardwareconfigurations, as explained above.

FIG. 15 is a high-level schematic illustration of a composite devicetarget 700, according to some embodiments of the invention. FIG. 15illustrates in a schematic manner, the combination of the conceptsdisclosed above to yield direct metrology measurements in a device area50 (see below method 900). As illustrated in FIG. 15, a device regionmay be regarded as being multi-layered, quasi-periodic and as having nooffsets and/or offsets only in non-critical direction only. Thisunderstanding is surprising, as metrology target designs are usuallyvery different from device designs. However, the inventors have foundout, that from this perspective and/or by selecting specific deviceregions according to these criteria, actual device regions may besuccessfully treated and measured as metrology targets or parts thereofand provide useable metrology results, directly relating to devicecharacteristics. By considering and/or selecting device regions as beingmulti-layered, quasi-periodic in the sense described above and as havingno offsets and/or offsets only in non-critical direction only—therespective device regions may be used as target or target parts byapplying methods 100, 600 and 800 respectively, possibly enhanced usingmachine learning algorithms 150, as illustrated schematically in FIG.15.

For example, target 700 may comprise at least one region 50A of device50 as part 710 of the measured target in which no offsets are introduced(at least along critical directions with respect to the device'sfunctionality) and adjacent cells 720 which have intended offsets thatmay be used to derive device overlays according to pre-calculatedsensitivity parameters and/or calibration function(s). FIG. 15schematically illustrates an option of selecting one region 50A astarget part 710 adjacent to cells 720, as well as an option of selectingmultiple regions 50A as target part, possibly but not necessarilyadjacent to cells 720. Regions 50A may be selected to yield a derivedpupil plane image from respective pupil images of regions 50A, e.g., anaverage or a weighted average thereof, which satisfies a specifiedcriterion such as a noise threshold, a periodicity threshold or anyalgorithmic threshold used to optimize the selection in view of thequality of the derived signals and related overlay derivations.

Alternatively or complementarily, target 700 may comprise at least oneregion 50B of device 50 as target 700 or as a part thereof, which hasintended offsets introduced along non-critical direction(s) of thedevice design at specific region 50B. Such intended offsets may beselected to provide useful metrology information (e.g., sensitivityparameter A) without damaging the device performance (see explanationand derivation above, corresponding to FIGS. 9-11). Target 700 mayfurther comprise adjacent cells 720 which have intended offsets that maybe used to derive device overlays according to pre-calculatedsensitivity parameters and/or calibration function(s).

Direct device measurements may further utilize calibration targets 750,set e.g., on scribe lines, which calibrate any of the effects ofmulti-layers, quasi-periodicity and sensitivity, as explained above.Moreover, methods 100, 600 and/or 800 (possibly carried out at leastpartly and/or enhanced using machine learning algorithms 150) may beimplemented synergistically as a method 900 described below, to enabledirect measurements of metrology parameters on devices which aremulti-layered and non-periodic without introducing offsets alongcritical direction of the device designs.

FIG. 16 is a high-level flowchart illustrating an integrative method 900of measuring device overlays directly on the device, according to someembodiments of the invention. Method 900 may be carried out at leastpartly and/or enhanced using machine learning algorithms. Method 900 maybe at least partly implemented by at least one computer processor, e.g.,in a metrology module. Certain embodiments comprise computer programproducts comprising a computer readable storage medium having computerreadable program embodied therewith and configured to carry out of therelevant stages of method 900. Certain embodiments comprise targetdesign files of respective targets designed by embodiments of method900.

Method 900 may comprise using reference calibration targets and/ordevice-adjacent cells with intended offsets to enable direct measurementof device parts without introducing offsets into the device design(stage 910), e.g., implementing method 800. Method 900 may compriseapplying machine learning algorithms to calibrate sensitivity using atleast one of: introducing offsets along non-critical direction, usingadjacent target cells with introduced offsets, and using sensitivitycalibration targets on scribe lines (stage 915), as explained above.

Method 900 may comprise extending the cell designs to multi-layeredmeasurements (stage 920), e.g., implementing method 100 in any of itsvariations 100A-D, possibly enhanced by machine learning algorithms.Method 900 may comprise configuring additional targets to providelayer-specific metrology parameters using multi-layered (N) target cells(having N>2 overlapping layers) comprising at least one of: N cellpairs, each pair with opposite offsets at a different layer; N cellswith selected intended offsets; N−1 or fewer cells with selectedintended offsets configured to utilize pupil information; andcalibration targets alongside overlay targets with down to 2 cells(stage 925), as explained above. In certain embodiments, targets maycomprises a single cell per target, with the machine learningalgorithm(s) being further configured to enable model-free on-the-flyoptical overlay measurements of the single cell.

Method 900 may comprise measuring quasi-periodic design patternsdirectly while managing and bounding resulting inaccuracies (stage 930),e.g., implementing method 600. Method 900 may comprise measuringmetrology parameters from at least a portion of a device design that isselected to have a plurality of irregularly repeating units, havingdifferent sets of lines and cuts as exemplary specified features, alongat least one direction of the portion (stage 935), as explained above.

Method 900 may comprise integrating the derivations for offset-less,multi-layer and quasi-periodic measurement algorithms (stage 940) todirectly measuring metrology parameters on devices (stage 950).

Method 900 may further comprise selecting parameters of the adjacenttarget cells and/or the sensitivity calibration targets to reduceinaccuracy according to a model of the inaccuracy (stage 955), asillustrated above.

Corresponding metrology targets comprise at least a portion of a devicedesign 710 that is selected to have a plurality of irregularly repeatingunits (e.g., as schematically exemplified in patterns 420A-E), havingdifferent sets of lines and cuts (e.g., as schematically exemplified intargets 400), along at least one direction of the portion, and aplurality of additional cells comprising multi-layer calibration cells(e.g., as schematically exemplified in targets 200 and 300) andsensitivity calibration cells (e.g., as schematically exemplified intargets 700 and 750). The multi-layer calibration cells may comprise anyof (see FIG. 1A): N cell pairs, each pair with opposite offsets at adifferent layer; N cells with selected intended offsets; N cells withselected intended offsets (or possibly fewer cells depending on themeasurement conditions and on algorithms used) configured to utilizepupil information; and N-cell calibration targets alongside overlaytargets with N−1 cells and optionally down to 2 cells, depending oncalibration conditions and algorithmic complexity. The sensitivitycalibration cells may comprise at least two additional cells havingintended offsets along the critical measurement direction of the atleast one target cell, e.g., additional cells having an orthogonalcritical measurement direction with respect to the at least one targetcell. The at least two additional cells may be adjacent to the deviceportion as additional cells 720 are. Respective metrology measurementsof the disclosed targets are also considered part of the presentdisclosure.

FIG. 17 is a high-level flowchart illustrating a method 960 of applyingmachine learning algorithms to any of the disclosed methods, accordingto some embodiments of the invention. Method 960 and/or stages thereofmay be integrated in any of the methods disclosed above, and may becarried out at least partly and/or enhanced using machine learningalgorithms. Method 960 may be at least partly implemented by at leastone computer processor, e.g., in a metrology module. Certain embodimentscomprise computer program products comprising a computer readablestorage medium having computer readable program embodied therewith andconfigured to carry out of the relevant stages of method 960. Certainembodiments comprise target design files of respective targets designedby embodiments of method 960.

Method 960 and/or any of methods 100, 600, 800 and 900 may comprise anyof the following stages: applying machine learning algorithm(s) tocalibrate measurement sensitivity (stage 962), using machine learningalgorithm(s) to reduce the required number of cells (stage 965),training the machine learning algorithm(s) on simulation targets (stage970), optimizing target design, using training results, to matchspecific device patterns (stage 975), matching the behavior of targetdesigns to specific device patterns' behavior (stage 977) and usingmachine learning algorithm(s) to enable model-free on-the-flymeasurements of single cells (stage 980).

Aspects of the present invention are described above with reference toflowchart illustrations and/or portion diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each portion of the flowchartillustrations and/or portion diagrams, and combinations of portions inthe flowchart illustrations and/or portion diagrams, can be implementedby computer program instructions. These computer program instructionsmay be provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or portion diagram portion or portions.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or portiondiagram portion or portions.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/orportion diagram portion or portions.

The aforementioned flowchart and diagrams illustrate the architecture,functionality, and operation of possible implementations of systems,methods and computer program products according to various embodimentsof the present invention. In this regard, each portion in the flowchartor portion diagrams may represent a module, segment, or portion of code,which comprises one or more executable instructions for implementing thespecified logical function(s). It should also be noted that, in somealternative implementations, the functions noted in the portion mayoccur out of the order noted in the figures. For example, two portionsshown in succession may, in fact, be executed substantiallyconcurrently, or the portions may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each portion of the portion diagrams and/or flowchart illustration,and combinations of portions in the portion diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

In the above description, an embodiment is an example or implementationof the invention. The various appearances of “one embodiment”, “anembodiment”, “certain embodiments” or “some embodiments” do notnecessarily all refer to the same embodiments. Although various featuresof the invention may be described in the context of a single embodiment,the features may also be provided separately or in any suitablecombination. Conversely, although the invention may be described hereinin the context of separate embodiments for clarity, the invention mayalso be implemented in a single embodiment. Certain embodiments of theinvention may include features from different embodiments disclosedabove, and certain embodiments may incorporate elements from otherembodiments disclosed above. The disclosure of elements of the inventionin the context of a specific embodiment is not to be taken as limitingtheir use in the specific embodiment alone. Furthermore, it is to beunderstood that the invention can be carried out or practiced in variousways and that the invention can be implemented in certain embodimentsother than the ones outlined in the description above.

The invention is not limited to those diagrams or to the correspondingdescriptions. For example, flow need not move through each illustratedbox or state, or in exactly the same order as illustrated and described.Meanings of technical and scientific terms used herein are to becommonly understood as by one of ordinary skill in the art to which theinvention belongs, unless otherwise defined. While the invention hasbeen described with respect to a limited number of embodiments, theseshould not be construed as limitations on the scope of the invention,but rather as exemplifications of some of the preferred embodiments.Other possible variations, modifications, and applications are alsowithin the scope of the invention. Accordingly, the scope of theinvention should not be limited by what has thus far been described, butby the appended claims and their legal equivalents.

What is claimed is:
 1. A method of directly measuring metrology parameters on devices, the method comprising: measuring at least one metrology parameter from at least one portion of a device design that is selected to have a plurality of irregularly repeating units, having specified features, along at least one direction of the at least one portion, and applying at least one machine learning algorithm to calibrate sensitivity using at least one of: an intensity of diffraction orders orthogonal to the at least one direction, introduced offsets along a non-critical direction, target cells with introduced offsets adjacent to the device portion(s), and at least one sensitivity calibration target, wherein the measuring is carried out scatterometrically on a plurality of targets to provide layer-specific metrology parameters, at least one of the targets being part of the at least one device portion having N>2 overlapping layers, wherein the plurality of targets comprises at least one of: N cell pairs, each pair having opposite offsets at a different layer; N cells with selected intended offsets; N or fewer cells with selected intended offsets configured to utilize pupil information; and N-cell calibration targets alongside between N−1 and two overlay targets.
 2. The method of claim 1, wherein the plurality of targets comprises n<2N cells, and wherein the applying at least one machine learning algorithm is further configured to extract overlay information from the n<2N cells.
 3. (canceled)
 4. The method of claim 1, wherein the plurality of targets comprises a single cell per target, and wherein the at least one machine learning algorithm is further configured to enable model-free on-the-fly optical overlay measurements of the single cell.
 5. The method of claim 1, wherein the introduced offsets are orthogonal to a critical direction of the portion of the device and wherein the measuring the at least one metrology parameter is carried out without introducing an intended offset along the critical direction of the device portion.
 6. The method of claim 5, wherein a measurement direction of the adjacent target cells is perpendicular to the critical direction portion of the device portion.
 7. The method of claim 1, further comprising selecting parameters of at least one of the adjacent target cells and the sensitivity calibration targets, to reduce inaccuracy according to parameters of the at least one machine learning algorithm.
 8. The method of claim 1, wherein the at least one sensitivity calibration target is on scribe lines.
 9. (canceled)
 10. The method of claim 1, wherein the at least one machine learning algorithm is configured to learn an overlay signal using reference measurements and the method further comprises identifying and reducing a signal contamination due to target irregularities from the overlay signal, using the at least one machine learning algorithm.
 11. The method of claim 1, wherein the at least one machine learning algorithm is further configured to estimate a noise in the measurements of the at least one metrology parameter, wherein the noise results from irregularities of the device portion, being its deviations from strict periodicity, and to estimate a measurement error accordingly.
 12. The method of claim 11, wherein the at least one portion comprises a plurality of device design portions selected to yield a derived pupil plane image or a derived field plane image from respective pupil images of the portions, which satisfies a specified criterion with respect to the estimated noise.
 13. (canceled)
 14. The method of claim 1, wherein the targets comprise N cells with selected intended offsets, at least one of the cells being part of the device portion and having a zero intended offset.
 15. The method of claim 1, wherein the multi-layered targets comprise N or fewer cells with selected intended offsets configured to utilize pupil information, at least one of the cells being part of the device portion and having a zero intended offset.
 16. The method of claim 1, wherein the targets comprise N-cell calibration targets alongside between N−1 and two overlay targets at least one of the overlay targets being part of the device portion and having a zero intended offset.
 17. The method of claim 1, wherein the measuring is of M≥N differential signals to of which the at least one machine learning algorithm is applied.
 18. The method of claim 18, wherein the measuring of M signals is carried out sequentially for consecutive layers of the targets.
 19. The method of claim 18, wherein the measuring of M signals is carried out simultaneously for the N layers, by carrying out the measuring at a pupil plane with respect to the targets and using measurements of a plurality of pixel positions at the pupil plane.
 20. (canceled)
 21. The method of claim 1, further comprising deriving, based on parameters of the at least one machine learning algorithm, at least one device-like pattern from a respective at least one device design, and designing at least one of the targets to have regions between periodic structures thereof at least partially filled by the at least one device-like pattern.
 22. The method of claim 21, further comprising designing the at least one target to have sub-regions between elements of the periodic structures at least partially filled by the at least one device-like pattern.
 23. (canceled)
 24. A computer program product comprising a computer readable storage medium having computer readable program embodied therewith and configured to carry out at least partially the method of claim
 1. 25. A metrology module comprising the computer program product of claim
 24. 26. A target design file of targets designed according parameters of the at least one machine learning algorithm applied in the method of claim
 1. 27. A metrology target comprising: at least one portion of a device design having N>2 overlapping layers, which is selected to have a plurality of irregularly repeating units, having specified features, along at least one direction of the portion, and a plurality of additional cells comprising at least multi-layer cells and sensitivity calibration cells, wherein the multi-layer cells comprise at least one of: N cells with selected intended offsets; N or fewer cells with selected intended offsets configured to utilize pupil information; and N-cell calibration targets alongside between N−1 and two overlay targets, wherein the cells are configured according to parameters of at least one machine learning algorithm applied on measurements and/or simulations of the metrology target.
 28. The metrology target of claim 27, wherein the sensitivity calibration cells comprise at least two target cells with introduced offsets that area adjacent to the device portion.
 29. The metrology target of claim 28, wherein the introduced offsets are orthogonal to a critical direction of the portion of the device and wherein the device portion has no intended offset along the critical direction thereof.
 30. The metrology target of claim 28, wherein the parameters of at least one of the adjacent target cells and the sensitivity calibration targets are selected, according to the parameters of the at least one machine learning algorithm, to reduce inaccuracy according to a model of the inaccuracy.
 31. The metrology target of claim 27, wherein the sensitivity calibration cells are on scribe lines.
 32. The metrology target of claim 27, wherein the at least one portion comprises a plurality of device design portions selected to yield a derived pupil plane image from respective pupil images of the portions, which satisfies a specified criterion.
 33. The metrology target of claim 27, wherein the specified features comprise a least one set of lines and cuts.
 34. A target design file of the metrology target of claim
 27. 35. (canceled)
 36. A method comprising: configuring a multi-layered metrology target to have a plurality, M, of target cells over at least three, N≤M, target layers, each cell having at least one periodic structure in each layer, and configuring the periodic structures of each cell to be offset with respect to each other by specified offsets, measuring, scatterometrically, at least M differential signals from the multi-layered metrology target, and applying at least one machine learning algorithm to the differential signals and to the specified offsets, to calculate Scatterometry Overlay (SCOL) metrology parameters from the M measurements of the multi-layered metrology target by solving a set of M equations that relate the SCOL metrology parameters to the differential signals and to the specified offsets.
 37. The method of claim 36, wherein the multi-layered metrology target comprises M<2N cells, and wherein the applying at least one machine learning algorithm is further configured to extract overlay information from the M<2N cells.
 38. (canceled)
 39. (canceled)
 40. The method of claim 36, wherein the multi-layered metrology target comprises a single cell per target, and wherein the at least one machine learning algorithm is further configured to enable model-free on-the-fly optical overlay measurements of the single cell.
 41. The method of claim 36, wherein the SCOL metrology parameters are overlays between the N layers.
 42. The method of claim 36, wherein the application of the at least one machine learning algorithm to calculate the SCOL metrology parameters is carried out sequentially for consecutive layers.
 43. The method of claim 36, wherein the application of the at least one machine learning algorithm to calculate the SCOL metrology parameters is carried out simultaneously for the layers, by carrying out the measuring at a pupil plane with respect to the target and using measurements of a plurality of pixel positions at the pupil plane.
 44. (canceled)
 45. A computer program product comprising a computer readable storage medium having computer readable program embodied therewith and configured to carry out at least partially the method of claim
 36. 46. A metrology module comprising the computer program product of claim
 45. 47. A target design file of targets designed according to the method of claim
 36. 48. (canceled)
 49. A multi-layered metrology target comprising a plurality of target cells over at least three target layers, each cell having at least one periodic structure in each layer, wherein the periodic structures of each cell are offset with respect to each other by specified offsets, wherein the cells are configured according to parameters of at least one machine learning algorithm applied on measurements and/or simulations of the metrology target.
 50. (canceled)
 51. A method comprising measuring at least one metrology parameter in at least one target cell without introducing an intended offset along a critical measurement direction into the at least one target cell by applying at least one machine learning algorithm to calibrate at least one sensitivity parameter using offsets in at least one of: (i) an orthogonal, non-critical measurement direction and (ii) at least one additional target cell other than the at least one target cell.
 52. (canceled)
 53. The method of claim 51, wherein the offsets in the orthogonal direction are introduced into at least one additional target cell other than the at least one target cell.
 54. The method of claim 53, wherein the at least one additional target cell is adjacent to the at least one target cell.
 55. The method of claim 53, wherein the at least one additional target cell is a calibration target positioned on scribe lines.
 56. The method of claim 53, further comprising selecting, according parameters of the at least one machine learning algorithm, parameters of the at least one additional target cell to reduce inaccuracy according to a model of the inaccuracy.
 57. The method of claim 51, wherein the at least one target cell comprises at least a part of a device design.
 58. The method of claim 57, further comprising introducing the offsets in the orthogonal direction into at least one additional target cell adjacent to the at least one target cell, according parameters of the at least one machine learning algorithm.
 59. The method of claim 57, wherein the offsets are introduced along the orthogonal, non-critical measurement direction of the device design, according parameters of the at least one machine learning algorithm.
 60. The method of claim 57, further comprising introducing the offsets in at least one calibration target positioned on scribe lines.
 61. A computer program product comprising a computer readable storage medium having computer readable program embodied therewith and configured to carry out at least partially the method of claim
 51. 62. A metrology target comprising: at least one target cell without an intended offset along a critical measurement direction of the at least one target cell, and at least two additional cells having intended offsets along the critical measurement direction of the at least one target cell, wherein the intended offsets are derived according to the parameters of at least one machine learning algorithm applied to measurements and/or simulations of metrology measurements of the metrology target.
 63. The metrology target of claim 62, wherein the at least two additional cells have an orthogonal critical measurement direction with respect to the at least one target cell.
 64. The metrology target of claim 62, wherein the at least two additional cells are adjacent to the at least one target cell.
 65. The metrology target of claim 62, wherein the at least two additional cells are calibration targets on scribe lines.
 66. The metrology target of claim 62, wherein the at least one target cell comprises at least a part of a device design.
 67. (canceled)
 68. A metrology target comprising at least one target cell without an intended offset along a critical measurement direction of the at least one target cell, and having intended offsets along a non-critical measurement direction of the at least one target cell, wherein the intended offsets are derived according to the parameters of at least one machine learning algorithm applied to measurements and/or simulations of metrology measurements of the metrology target.
 69. (canceled) 